{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Image features exercise\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "We have seen that we can achieve reasonable performance on an image classification task by training a linear classifier on the pixels of the input image. In this exercise we will show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.\n",
    "\n",
    "All of your work for this exercise will be done in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Load data\n",
    "Similar to previous exercises, we will load CIFAR-10 data from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.features import color_histogram_hsv, hog_feature\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "       del X_train, y_train\n",
    "       del X_test, y_test\n",
    "       print('Clear previously loaded data.')\n",
    "    except:\n",
    "       pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "    \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "    \n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Extract Features\n",
    "For each image we will compute a Histogram of Oriented\n",
    "Gradients (HOG) as well as a color histogram using the hue channel in HSV\n",
    "color space. We form our final feature vector for each image by concatenating\n",
    "the HOG and color histogram feature vectors.\n",
    "\n",
    "Roughly speaking, HOG should capture the texture of the image while ignoring\n",
    "color information, and the color histogram represents the color of the input\n",
    "image while ignoring texture. As a result, we expect that using both together\n",
    "ought to work better than using either alone. Verifying this assumption would\n",
    "be a good thing to try for your own interest.\n",
    "\n",
    "The `hog_feature` and `color_histogram_hsv` functions both operate on a single\n",
    "image and return a feature vector for that image. The extract_features\n",
    "function takes a set of images and a list of feature functions and evaluates\n",
    "each feature function on each image, storing the results in a matrix where\n",
    "each column is the concatenation of all feature vectors for a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true,
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done extracting features for 1000 / 49000 images\n",
      "Done extracting features for 2000 / 49000 images\n",
      "Done extracting features for 3000 / 49000 images\n",
      "Done extracting features for 4000 / 49000 images\n",
      "Done extracting features for 5000 / 49000 images\n",
      "Done extracting features for 6000 / 49000 images\n",
      "Done extracting features for 7000 / 49000 images\n",
      "Done extracting features for 8000 / 49000 images\n",
      "Done extracting features for 9000 / 49000 images\n",
      "Done extracting features for 10000 / 49000 images\n",
      "Done extracting features for 11000 / 49000 images\n",
      "Done extracting features for 12000 / 49000 images\n",
      "Done extracting features for 13000 / 49000 images\n",
      "Done extracting features for 14000 / 49000 images\n",
      "Done extracting features for 15000 / 49000 images\n",
      "Done extracting features for 16000 / 49000 images\n",
      "Done extracting features for 17000 / 49000 images\n",
      "Done extracting features for 18000 / 49000 images\n",
      "Done extracting features for 19000 / 49000 images\n",
      "Done extracting features for 20000 / 49000 images\n",
      "Done extracting features for 21000 / 49000 images\n",
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      "Done extracting features for 27000 / 49000 images\n",
      "Done extracting features for 28000 / 49000 images\n",
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      "Done extracting features for 38000 / 49000 images\n",
      "Done extracting features for 39000 / 49000 images\n",
      "Done extracting features for 40000 / 49000 images\n",
      "Done extracting features for 41000 / 49000 images\n",
      "Done extracting features for 42000 / 49000 images\n",
      "Done extracting features for 43000 / 49000 images\n",
      "Done extracting features for 44000 / 49000 images\n",
      "Done extracting features for 45000 / 49000 images\n",
      "Done extracting features for 46000 / 49000 images\n",
      "Done extracting features for 47000 / 49000 images\n",
      "Done extracting features for 48000 / 49000 images\n",
      "Done extracting features for 49000 / 49000 images\n"
     ]
    }
   ],
   "source": [
    "from cs231n.features import *\n",
    "\n",
    "num_color_bins = 10 # Number of bins in the color histogram\n",
    "feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]\n",
    "X_train_feats = extract_features(X_train, feature_fns, verbose=True)\n",
    "X_val_feats = extract_features(X_val, feature_fns)\n",
    "X_test_feats = extract_features(X_test, feature_fns)\n",
    "\n",
    "# Preprocessing: Subtract the mean feature\n",
    "mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats -= mean_feat\n",
    "X_val_feats -= mean_feat\n",
    "X_test_feats -= mean_feat\n",
    "\n",
    "# Preprocessing: Divide by standard deviation. This ensures that each feature\n",
    "# has roughly the same scale.\n",
    "std_feat = np.std(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats /= std_feat\n",
    "X_val_feats /= std_feat\n",
    "X_test_feats /= std_feat\n",
    "\n",
    "# Preprocessing: Add a bias dimension\n",
    "X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])\n",
    "X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])\n",
    "X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train SVM on features\n",
    "Using the multiclass SVM code developed earlier in the assignment, train SVMs on top of the features extracted above; this should achieve better results than training SVMs directly on top of raw pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best validation accuracy achieved during cross-validation: 0.418000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune the learning rate and regularization strength\n",
    "\n",
    "from cs231n.classifiers.linear_classifier import LinearSVM\n",
    "\n",
    "learning_rates = [1e-9, 1e-8, 1e-7]\n",
    "regularization_strengths = [5e4, 5e5, 5e6]\n",
    "\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_svm = None\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained classifer in best_svm. You might also want to play          #\n",
    "# with different numbers of bins in the color histogram. If you are careful    #\n",
    "# you should be able to get accuracy of near 0.44 on the validation set.       #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "for lr in learning_rates:\n",
    "    for reg in regularization_strengths:\n",
    "        svm = LinearSVM()\n",
    "        svm.train(X_train_feats, y_train, learning_rate=lr, reg=reg, num_iters=1500)\n",
    "        \n",
    "        train_pred = svm.predict(X_train_feats)\n",
    "        val_pred = svm.predict(X_val_feats)\n",
    "        \n",
    "        train_acc = np.mean(train_pred == y_train)\n",
    "        val_acc = np.mean(val_pred == y_val)\n",
    "        \n",
    "        if val_acc > best_val:\n",
    "            best_val = val_acc\n",
    "            best_svm = svm\n",
    "\n",
    "pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "svm_test_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.423\n"
     ]
    }
   ],
   "source": [
    "# Evaluate your trained SVM on the test set: you should be able to get at least 0.40\n",
    "y_test_pred = best_svm.predict(X_test_feats)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print(test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 80 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An important way to gain intuition about how an algorithm works is to\n",
    "# visualize the mistakes that it makes. In this visualization, we show examples\n",
    "# of images that are misclassified by our current system. The first column\n",
    "# shows images that our system labeled as \"plane\" but whose true label is\n",
    "# something other than \"plane\".\n",
    "\n",
    "examples_per_class = 8\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for cls, cls_name in enumerate(classes):\n",
    "    idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]\n",
    "    idxs = np.random.choice(idxs, examples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)\n",
    "        plt.imshow(X_test[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls_name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "### Inline question 1:\n",
    "Describe the misclassification results that you see. Do they make sense?\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network on image features\n",
    "Earlier in this assigment we saw that training a two-layer neural network on raw pixels achieved better classification performance than linear classifiers on raw pixels. In this notebook we have seen that linear classifiers on image features outperform linear classifiers on raw pixels. \n",
    "\n",
    "For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 155)\n",
      "(49000, 154)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: Remove the bias dimension\n",
    "# Make sure to run this cell only ONCE\n",
    "print(X_train_feats.shape)\n",
    "X_train_feats = X_train_feats[:, :-1]\n",
    "X_val_feats = X_val_feats[:, :-1]\n",
    "X_test_feats = X_test_feats[:, :-1]\n",
    "\n",
    "print(X_train_feats.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1960: loss 2.302585\n",
      "iteration 100 / 1960: loss 1.490777\n",
      "iteration 200 / 1960: loss 1.242759\n",
      "iteration 300 / 1960: loss 1.327560\n",
      "iteration 400 / 1960: loss 1.162959\n",
      "iteration 500 / 1960: loss 1.198804\n",
      "iteration 600 / 1960: loss 1.261497\n",
      "iteration 700 / 1960: loss 1.256779\n",
      "iteration 800 / 1960: loss 1.242419\n",
      "iteration 900 / 1960: loss 1.103904\n",
      "iteration 1000 / 1960: loss 1.211121\n",
      "iteration 1100 / 1960: loss 1.098097\n",
      "iteration 1200 / 1960: loss 1.061823\n",
      "iteration 1300 / 1960: loss 1.157242\n",
      "iteration 1400 / 1960: loss 0.942130\n",
      "iteration 1500 / 1960: loss 0.917848\n",
      "iteration 1600 / 1960: loss 0.867976\n",
      "iteration 1700 / 1960: loss 1.020859\n",
      "iteration 1800 / 1960: loss 0.877897\n",
      "iteration 1900 / 1960: loss 1.078518\n",
      "Validation accuracy:  0.554\n"
     ]
    }
   ],
   "source": [
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "input_dim = X_train_feats.shape[1]\n",
    "hidden_dim = 350\n",
    "num_classes = 10\n",
    "\n",
    "net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "best_net = None\n",
    "\n",
    "################################################################################\n",
    "# TODO: Train a two-layer neural network on image features. You may want to    #\n",
    "# cross-validate various parameters as in previous sections. Store your best   #\n",
    "# model in the best_net variable.                                              #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "net.train(X_train_feats, y_train, X_val_feats, y_val, \n",
    "          learning_rate=0.8, learning_rate_decay=0.95, \n",
    "          reg=2.5e-4, \n",
    "          num_iters=1960, batch_size=200, verbose=True)\n",
    "\n",
    "# Predict on the validation set\n",
    "val_acc = (net.predict(X_val_feats) == y_val).mean()\n",
    "print('Validation accuracy: ', val_acc)\n",
    "\n",
    "pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_net = net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "id": "nn_test_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.588\n"
     ]
    }
   ],
   "source": [
    "# Run your best neural net classifier on the test set. You should be able\n",
    "# to get more than 55% accuracy.\n",
    "\n",
    "test_acc = (best_net.predict(X_test_feats) == y_test).mean()\n",
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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